OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive KDE for Real-Time Thresholding: Prioritized Queues for Financial Crime Investigation
Venue: arXiv cs.LG 2026
Year: 2026

We study the problem of converting a continuous stream of risk scores into stable decision thresholds under non-stationary score distributions. This problem arises in a wide range of detection systems where scores must be partitioned into prioritized processing regions while preserving semantic consistency over time.
A benchmarking framework for PON-based fronthaul network design
Venue: arXiv cs.NI 2026
Year: 2026

As mobile networks transition toward 5G and 6G RAN architectures, Passive Optical Networks (PONs) offer a critical solution for cost-effective fronthaul transport. However, the lack of standardized evaluation models in current literature makes an objective comparison of diverse optimization strategies difficult. This paper addresses this gap by proposing a unified benchmarking framework that standardizes cost catalogs and deployment scenarios. We formulate the network design problem using Integer Linear Programming (ILP) to establish optimality bounds and evaluate three scalable heuristic strategies: a Genetic Algorithm, K-Means Clustering (KMC+), and a graph-based Randomized Successive Splitter Assignment (RSSA+) algorithm. Simulation results show that a time-limited ILP remains a strong reference point, even when optimality is not reached. Despite being rarely used in prior fronthaul planning studies, it consistently yields solutions superior to those produced by standard heuristic methods. Among scalable approaches, RSSA+ reliably attains near-ILP performance while ensuring feasibility across all evaluated scenarios, which underscores the importance of advanced, constraint-aware algorithmic designs over simpler heuristics. The complete benchmarking framework and datasets are publicly shared in [1].
European digital identity: A missed opportunity?
Venue: arXiv cs.CR 2026
Year: 2026

Recent European efforts around digital identity -- the EUDI regulation and its OpenID architecture -- aim high, but start from a narrow and ill-defined conceptualization of authentication. Based on a broader, more grounded understanding of the term, in we identify several issues in the design of OpenID4VCI and OpenID4VP: insecure practices, static, and subject-bound credential types, and a limited query language restrict their application to classic scenarios of credential exchange -- already supported by existing solutions like OpenID Connect, SIOPv2, OIDC4IDA, and OIDC Claims Aggregation -- barring dynamic, asynchronous, or automated use cases. We also debunk OpenID's 'paradigm-shifting' trust-model, which -- when compared to existing decentralized alternatives -- does not deliver any significant increase in control, privacy, and portability of personal information. Not only the technical choices limit the capabilities of the EUDI framework; also the legislation itself cannot accommodate the promise of self-sovereign identity. In particular, we criticize the introduction of institutionalized trusted lists, and discuss their economical and political risks. Their potential to decline into an exclusory, re-centralized ecosystem endangers the vision of a user-oriented identity management in which individuals are in charge. Instead, the consequences might severely restrict people in what they can do with their personal information, and risk increased linkability and monitoring. In anticipation of revisions to the EUDI regulations, we suggest several technical alternatives that overcome some of the issues with the architecture of OpenID. In particular, OAuth's UMA extension and its A4DS profile, as well as their integration in GNAP, are worth looking into. Future research into uniform query (meta-)languages is needed to address the heterogeneity of attestations and providers.
Transparent Malware Detection With Granular Assembly Flow Explainability via Graph Neural Networks
Venue: arXiv cs.CR 2026
Year: 2026

As malware continues to become increasingly sophisticated, threatening, and evasive, malware detection systems must keep pace and become equally intelligent, powerful, and transparent. In this paper, we propose Assembly Flow Graph (AFG) to comprehensively represent the assembly flow of a binary executable as graph data. Importantly, AFG can be used to extract granular explanations needed to increase transparency for malware detection using Graph Neural Networks (GNNs). However, since AFGs may be large in practice, we also propose a Meta-Coarsening approach to improve computational tractability via graph reduction. To evaluate our proposed approach we consider several novel and existing metrics to quantify the granularity and quality of explanations. Lastly, we also consider several hyperparameters in our proposed Meta-Coarsening approach that can be used to control the final explanation size. We evaluate our proposed approach using the CIC-DGG-2025 dataset. Our results indicate that our proposed AFG and Meta-Coarsening approach can provide both increased explainability and inference performance at certain coarsening levels. However, most importantly, to the best of our knowledge, we are the first to consider granular explainability in malware detection using GNNs.
Aiming for AI Interoperability: Challenges and Opportunities
Venue: arXiv cs.CY 2026
Year: 2026

The Aiming for AI Interoperability report investigates the ongoing challenge of achieving regulatory and technical AI interoperability as national and global AI governance efforts are proliferating. Here, technical interoperability is the ability of AI systems and networks to function together, and regulatory interoperability is the consistency and overlap of rules across jurisdictions and sectors. This report observes an accelerating trend that many governments, standard-setting bodies, and private firms are drafting, implementing, or passing new AI laws, policies, and frameworks at a staggering pace, resulting in fragmentation and confusion for both private and public sector actors.
"Just in Time" World Modeling Supports Human Planning and Reasoning
Venue: arXiv cs.AI 2026
Year: 2026

Probabilistic mental simulation is thought to play a key role in human reasoning, planning, and prediction, yet the demands of simulation in complex environments exceed realistic human capacity limits. A theory with growing evidence is that people simulate using simplified representations of the environment that abstract away from irrelevant details, but it is unclear how people determine these simplifications efficiently. Here, we present a "Just-in-Time" framework for simulation-based reasoning that demonstrates how such representations can be constructed online with minimal added computation. The model uses a tight interleaving of simulation, visual search, and representation modification, with the current simulation guiding where to look and visual search flagging objects that should be encoded for subsequent simulation. Despite only ever encoding a small subset of objects, the model makes high-utility predictions. We find strong empirical support for this account over alternative models in a grid-world planning task and a physical reasoning task across a range of behavioral measures. Together, these results offer a concrete algorithmic account of how people construct reduced representations to support efficient mental simulation.
Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators
Venue: arXiv cs.LG 2026
Year: 2026

Partial differential equations (PDEs) are central to scientific modeling. Modern workflows increasingly rely on learning-based components to support model reuse, inference, and integration across large computational processes. Despite the emergence of various physics-aware data-driven approaches, the field still lacks a unified perspective to uncover their relationships, limitations, and appropriate roles in scientific workflows. To this end, we propose a unifying perspective to place two dominant paradigms: Physics-Informed Neural Networks (PINNs) and Neural Operators (NOs), within a shared design space. We organize existing methods from three fundamental dimensions: what is learned, how physical structures are integrated into the learning process, and how the computational load is amortized across problem instances. In this way, many challenges can be best understood as consequences of these structural properties of learning PDEs. By analyzing advances through this unifying view, our survey aims to facilitate the development of reliable learning-based PDE solvers and catalyze a synthesis of physics and data.
PAS-Mamba: Phase-Amplitude-Spatial State Space Model for MRI Reconstruction
Venue: arXiv cs.CV 2026
Year: 2026

Joint feature modeling in both the spatial and frequency domains has become a mainstream approach in MRI reconstruction. However, existing methods generally treat the frequency domain as a whole, neglecting the differences in the information carried by its internal components. According to Fourier transform theory, phase and amplitude represent different types of information in the image. Our spectrum swapping experiments show that magnitude mainly reflects pixel-level intensity, while phase predominantly governs image structure. To prevent interference between phase and magnitude feature learning caused by unified frequency-domain modeling, we propose the Phase-Amplitude-Spatial State Space Model (PAS-Mamba) for MRI Reconstruction, a framework that decouples phase and magnitude modeling in the frequency domain and combines it with image-domain features for better reconstruction. In the image domain, LocalMamba preserves spatial locality to sharpen fine anatomical details. In frequency domain, we disentangle amplitude and phase into two specialized branches to avoid representational coupling. To respect the concentric geometry of frequency information, we propose Circular Frequency Domain Scanning (CFDS) to serialize features from low to high frequencies. Finally, a Dual-Domain Complementary Fusion Module (DDCFM) adaptively fuses amplitude phase representations and enables bidirectional exchange between frequency and image domains, delivering superior reconstruction. Extensive experiments on the IXI and fastMRI knee datasets show that PAS-Mamba consistently outperforms state of the art reconstruction methods.
Exploiting Spot Instances for Time-Critical Cloud Workloads Using Optimal Randomized Strategies
Venue: arXiv cs.DC 2026
Year: 2026

This paper addresses the challenge of deadline-aware online scheduling for jobs in hybrid cloud environments, where jobs may run on either cost-effective but unreliable spot instances or more expensive on-demand instances, under hard deadlines. We first establish a fundamental limit for existing (predominantly-) deterministic policies, proving a worst-case competitive ratio of $Ω(K)$, where $K$ is the cost ratio between on-demand and spot instances. We then present a novel randomized scheduling algorithm, ROSS, that achieves a provably optimal competitive ratio of $\sqrt{K}$ under reasonable deadlines, significantly improving upon existing approaches. Extensive evaluations on real-world trace data from Azure and AWS demonstrate that ROSS effectively balances cost optimization and deadline guarantees, consistently outperforming the state-of-the-art by up to $30\%$ in cost savings, across diverse spot market conditions.
A Brain-inspired Embodied Intelligence for Fluid and Fast Reflexive Robotics Control
Venue: arXiv cs.RO 2026
Year: 2026

Recent advances in embodied intelligence have leveraged massive scaling of data and model parameters to master natural-language command following and multi-task control. In contrast, biological systems demonstrate an innate ability to acquire skills rapidly from sparse experience. Crucially, current robotic policies struggle to replicate the dynamic stability, reflexive responsiveness, and temporal memory inherent in biological motion. Here we present Neuromorphic Vision-Language-Action (NeuroVLA), a framework that mimics the structural organization of the bio-nervous system between the cortex, cerebellum, and spinal cord. We adopt a system-level bio-inspired design: a high-level model plans goals, an adaptive cerebellum module stabilizes motion using high-frequency sensors feedback, and a bio-inspired spinal layer executes lightning-fast actions generation. NeuroVLA represents the first deployment of a neuromorphic VLA on physical robotics, achieving state-of-the-art performance. We observe the emergence of biological motor characteristics without additional data or special guidance: it stops the shaking in robotic arms, saves significant energy(only 0.4w on Neuromorphic Processor), shows temporal memory ability and triggers safety reflexes in less than 20 milliseconds.
DesignBridge: Bridging Designer Expertise and User Preferences through AI-Enhanced Co-Design for Fashion
Venue: arXiv cs.HC 2026
Year: 2026

Effective collaboration between designers and users is important for fashion design, which can increase the user acceptance of fashion products and thereby create value. However, it remains an enduring challenge, as traditional designer-centric approaches restrict meaningful user participation, while user-driven methods demand design proficiency, often marginalizing professional creative judgment. Current co-design practices, including workshops and AI-assisted frameworks, struggle with low user engagement, inefficient preference collection, and difficulties in balancing user feedback with design considerations. To address these challenges, we conducted a formative study with designers and users experienced in co-design (N=7), identifying critical challenges for current collaboration between designers and users in the co-design process, and their requirements. Informed by these insights, we introduce DesignBridge, a multi-platform AI-enhanced interactive system that bridges designer expertise and user preferences through three stages: (1) Initial Design Framing, where designers define initial concepts. (2) Preference Expression Collection, where users intuitively articulate preferences via interactive tools. (3) Preference-Integrated Design, where designers use AI-assisted analytics to integrate feedback into cohesive designs. A user study demonstrates that DesignBridge significantly enhances user preference collection and analysis, enabling designers to integrate diverse preferences with professional expertise.
MIND: Empowering Mental Health Clinicians with Multimodal Data Insights through a Narrative Dashboard
Venue: arXiv cs.HC 2026
Year: 2026

Advances in data collection enable the capture of rich patient-generated data: from passive sensing (e.g., wearables and smartphones) to active self-reports (e.g., cross-sectional surveys and ecological momentary assessments). Although prior research has demonstrated the utility of patient-generated data in mental healthcare, significant challenges remain in effectively presenting these data streams along with clinical data (e.g., clinical notes) for clinical decision-making. Through co-design sessions with five clinicians, we propose MIND, a large language model-powered dashboard designed to present clinically relevant multimodal data insights for mental healthcare. MIND presents multimodal insights through narrative text, complemented by charts communicating underlying data. Our user study (N=16) demonstrates that clinicians perceive MIND as a significant improvement over baseline methods, reporting improved performance to reveal hidden and clinically relevant data insights (p<.001) and support their decision-making (p=.004). Grounded in the study results, we discuss future research opportunities to integrate data narratives in broader clinical practices.
MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks
Venue: arXiv cs.AI 2026
Year: 2026

While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliver. Such shortcomings stem from two key factors: (1) methodological complexity - agent orchestration is performed using sequential, code-level execution that limits global system-level holistic reasoning and scales poorly with agent complexity - and (2) efficacy uncertainty - MAS are deployed without understanding if there are tangible benefits compared to single-agent systems (SAS). We propose MASOrchestra, a training-time framework that formulates MAS orchestration as a function-calling reinforcement learning problem with holistic orchestration, generating an entire MAS at once. In MAS-Orchestra, complex, goal-oriented subagents are abstracted as callable functions, enabling global reasoning over system structure while hiding internal execution details. To rigorously study when and why MAS are beneficial, we introduce MASBENCH, a controlled benchmark that characterizes tasks along five axes: Depth, Horizon, Breadth, Parallel, and Robustness. Our analysis reveals that MAS gains depend critically on task structure, verification protocols, and the capabilities of both orchestrator and subagents, rather than holding universally. Guided by these insights, MAS-Orchestra achieves consistent improvements on public benchmarks including mathematical reasoning, multi-hop QA, and search-based QA, while achieving more than 10x efficiency over strong baselines. Together, MAS-Orchestra and MASBENCH enable better training and understanding of MAS in the pursuit of multi-agent intelligence.
Efficient Imputation for Patch-based Missing Single-cell Data via Cluster-regularized Optimal Transport
Venue: arXiv cs.LG 2026
Year: 2026

Missing data in single-cell sequencing datasets poses significant challenges for extracting meaningful biological insights. However, existing imputation approaches, which often assume uniformity and data completeness, struggle to address cases with large patches of missing data. In this paper, we present CROT, an optimal transport-based imputation algorithm designed to handle patch-based missing data in tabular formats. Our approach effectively captures the underlying data structure in the presence of significant missingness. Notably, it achieves superior imputation accuracy while significantly reducing runtime, demonstrating its scalability and efficiency for large-scale datasets. This work introduces a robust solution for imputation in heterogeneous, high-dimensional datasets with structured data absence, addressing critical challenges in both biological and clinical data analysis. Our code is available at Anomalous Github.
Say Anything but This: When Tokenizer Betrays Reasoning in LLMs
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) reason over discrete token ID sequences, yet modern subword tokenizers routinely produce non-unique encodings: multiple token ID sequences can detokenize to identical surface strings. This representational mismatch creates an unmeasured fragility wherein reasoning processes can fail. LLMs may treat two internal representations as distinct "words" even when they are semantically identical at the text level. In this work, we show that tokenization can betray LLM reasoning through one-to-many token ID mappings. We introduce a tokenization-consistency probe that requires models to replace designated target words in context while leaving all other content unchanged. The task is intentionally simple at the surface level, enabling us to attribute failures to tokenizer-detokenizer artifacts rather than to knowledge gaps or parameter limitations. Through analysis of over 11000 replacement trials across state-of-the-art open-source LLMs, we find a non-trivial rate of outputs exhibit phantom edits: cases where models operate under the illusion of correct reasoning, a phenomenon arising from tokenizer-induced representational defects. We further analyze these cases and provide a taxonomy of eight systematic tokenizer artifacts, including whitespace-boundary shifts and intra-word resegmentation. These findings indicate that part of apparent reasoning deficiency originates in the tokenizer layer, motivating tokenizer-level remedies before incurring the cost of training ever-larger models on ever-larger corpora.
GEGO: A Hybrid Golden Eagle and Genetic Optimization Algorithm for Efficient Hyperparameter Tuning in Resource-Constrained Environments
Venue: arXiv cs.NE 2026
Year: 2026

Hyperparameter tuning is a critical yet computationally expensive step in training neural networks, particularly when the search space is high dimensional and nonconvex. Metaheuristic optimization algorithms are often used for this purpose due to their derivative free nature and robustness against local optima. In this work, we propose Golden Eagle Genetic Optimization (GEGO), a hybrid metaheuristic that integrates the population movement strategy of Golden Eagle Optimization with the genetic operators of selection, crossover, and mutation. The main novelty of GEGO lies in embedding genetic operators directly into the iterative search process of GEO, rather than applying them as a separate evolutionary stage. This design improves population diversity during search and reduces premature convergence while preserving the exploration behavior of GEO. GEGO is evaluated on standard unimodal, multimodal, and composite benchmark functions from the CEC2017 suite, where it consistently outperforms its constituent algorithms and several classical metaheuristics in terms of solution quality and robustness. The algorithm is further applied to hyperparameter tuning of artificial neural networks on the MNIST dataset, where GEGO achieves improved classification accuracy and more stable convergence compared to GEO and GA. These results indicate that GEGO provides a balanced exploration-exploitation tradeoff and is well suited for hyperparameter optimization under constrained computational settings.
Beyond Denial-of-Service: The Puppeteer's Attack for Fine-Grained Control in Ranking-Based Federated Learning
Venue: arXiv cs.LG 2026
Year: 2026

Federated Rank Learning (FRL) is a promising Federated Learning (FL) paradigm designed to be resilient against model poisoning attacks due to its discrete, ranking-based update mechanism. Unlike traditional FL methods that rely on model updates, FRL leverages discrete rankings as a communication parameter between clients and the server. This approach significantly reduces communication costs and limits an adversary's ability to scale or optimize malicious updates in the continuous space, thereby enhancing its robustness. This makes FRL particularly appealing for applications where system security and data privacy are crucial, such as web-based auction and bidding platforms. While FRL substantially reduces the attack surface, we demonstrate that it remains vulnerable to a new class of local model poisoning attack, i.e., fine-grained control attacks. We introduce the Edge Control Attack (ECA), the first fine-grained control attack tailored to ranking-based FL frameworks. Unlike conventional denial-of-service (DoS) attacks that cause conspicuous disruptions, ECA enables an adversary to precisely degrade a competitor's accuracy to any target level while maintaining a normal-looking convergence trajectory, thereby avoiding detection. ECA operates in two stages: (i) identifying and manipulating Ascending and Descending Edges to align the global model with the target model, and (ii) widening the selection boundary gap to stabilize the global model at the target accuracy. Extensive experiments across seven benchmark datasets and nine Byzantine-robust aggregation rules (AGRs) show that ECA achieves fine-grained accuracy control with an average error of only 0.224%, outperforming the baseline by up to 17x. Our findings highlight the need for stronger defenses against advanced poisoning attacks. Our code is available at: https://github.com/Chenzh0205/ECA